Legacy system upgrading is a discomfort, but heath IT leaders and employees do it every day. Is it far off to completely modernize wellness IT?
Artificial intelligence is exploding in care. There have been a lot of operational applications for it, but there haven’t been many medical ones however. What might occur to encourage hospitals and health systems ‘ regular use of AI to become a common practice?
Everyone in the healthcare sector is aware that value-based worry is gradually becoming prevalent in the sector. But what can render it the economy standard? AI maybe?
Robert Connely has answers to all three of these concerns, and he claims that there will be changes for all three this time.
Connely, a supplier with a focus on enterprise AI decision-making and workflow automation, leads the world healthcare market. He has worked for multinational corporations like McKesson and Aetna and for successful health IT start-ups like Medicity for more than three decades in the areas of innovative, innovation, and proper leadership.
Redesign and remove, please
Healthcare service businesses this year will leave the “wrap and renew” approach to legacy systems in favor of targeted “reimagine and change” strategies, Connely predicts.
” The change in 2025 from updating legacy techniques to full development requires a fine equilibrium”, he said. The objective is to help organizations use AI to handle complex processes while reducing the professional debt associated with the maintenance of legacy systems.
” This change is likely to affect two key areas: the creation of development strategies themselves, and the rise of AI orchestration platforms, “he continued”. These components are unfolding instantly, but a unique pattern is emerging.”
Modernization has shifted away from the traditional” blow and replace “approach, which swaps archaic systems for newer ones – while effective, this method is very difficult, costly and time-consuming, often taking months just in the discovery phase only, he added.
He added that it frequently ignores the complex processes that modern businesses face, such as company lifecycle management and integrated patient care. These connected processes were not intended to be handled by legacy systems.
” A later view,’ cover and renew,’ sought to extend the life of legacy systems by integrating them with newer technologies, enabling their participation in complex workflows”, he added. This approach, however, continues to leave technical debt unresolved and only serves as a” cheat-and-burn” solution.
According to him, the emerging trend involves dividing up old systems into modular components and dispersing them between different technology layers, which will give rise to more flexibility and scalability.
” This is an approach we’re calling’ rethink and replace,’ and it uses generative AI to quickly and cost-effectively align business and IT to design and automate new workflow improvements”, he said. ” This capability extends low code technology, allowing organizations to accelerate time to value while reducing the time it takes to develop AI to perform complex orchestrations.”
” AI-powered orchestration platforms are a key enabler of this new approach, “he noted”. These platforms enable the integration of outdated systems with contemporary workflows, satiating current business, regulatory, and security requirements without requiring time-consuming and laborious retrofits. By creating a bridge between old and new, these platforms allow gradual modernization.”
He advised businesses to implement modern workflows while charting a clear path to eventually decommissioning outdated systems. Then, when does the technical debt justify keeping the legacy system more than the cost of maintaining it?
Organizations will create phased roadmaps that ultimately lead to the retirement of legacy systems as their functions continue to live on and evolve in newer platform environments in response to constantly changing needs, he said.
AI’s future depends on security
The future of AI in healthcare hinges on overcoming security concerns, especially around managing private patent data, and 2025 will be the tipping point, Connely said.
” I see a security breakthrough from two angles: technology and technique, “he predicted”. Currently, most AI in automation and decision making relies on statistical AI to predict, decide and automate workflows. To track behavior, improve performance, identify biases, and ensure responsible use, security concerns place a focus on model usage and auditing outputs.
” On the technology side, barriers to AI adoption include securing data for, and effectively managing and auditing statistical AI,” he continued. Retrieval-augmented generation frameworks enhance prompts for generative AI by incorporating private data from information systems.
He explained that this involves breaking data into chunks, vectorizing it, and embedding it in the prompt sent to the large language model. There is a mathematical chance of decoding the prompt, even though LLMs can be instructed not to use it for training.
” A promising solution lies in homomorphic encryption, a technique that allows data to remain encrypted while being processed by the AI model”, he explained. LLMs can use encrypted data that is then decrypted when they return to the source to produce augmented responses with this technology. However, this method is still a few years away from practical implementation. In the interim, more advanced strategies are being developed to secure AI use.
” One emerging technique involves the adoption of private LLMs, “he added”. Organizations are increasingly creating their own vector databases, embedding proprietary data that generative AI can access without exposing it outside the organization’s security boundaries. This approach enables businesses to benefit from generative AI without the drawbacks of using public tools like ChatGPT.
In addition, developers and integrators are applying AI narrowly within specific process workflows, he noted.
” This focused use limits exposure, reduces security risks and makes it easier to measure value,” Connely said”. Executives are finding ways to safely adopt AI while unlocking its potential value by combining these approaches, such as private LLMs, vector databases, and targeted AI applications.
AI boosts value-based care
In 2025, AI will be the catalyst that transforms value-based care from a pilot initiative to the standard model across healthcare, Connely predicted.
” Dissatisfaction with U. S. healthcare payers is at an all-time high, “he noted”. The U. S. is unique in having the most technically advanced – and expensive – medical system globally, yet it often serves as a safety net for non-medical challenges such as aging populations, social inequities, environmental factors and behavioral health issues. These realities are driving a shift from fee-for-service models to value-based care contracts.
“, requiring payers to adopt a more patient- and member-centric approach“, he continued. This approach acknowledges that a lot of healthcare use can be avoided. Often, minor issues–“ papercuts“ – escalate into costly medical problems when left unaddressed“.
Care management initiatives have already demonstrated that proactive interventions and frequent engagement can lower costs by lowering hospital stays, hospitalizations, and other high-cost services. However, there aren’t enough care managers to scale these efforts across entire populations.
” This is where AI steps in”, Connely said. ” AI can augment care management by engaging with members and their broader support networks, including caregivers, family, social services and providers. Through AI-driven orchestration, education and proactive intervention, health systems can address fragmented processes. Agentic AI platforms, which are more sophisticated systems capable of managing complex healthcare challenges and workflows, are examples of this.
” As VBC reshapes the payer’s role, requiring them to take greater responsibility for patient outcomes and journeys, technology becomes a critical enabler, “he continued”. For healthcare payer CEOs, this is top of mind. AI-driven systems allow for better engagement and coordination among providers, members and others in the healthcare ecosystem.”
These improvements, he said, are accelerating the transition to VBC by allowing payers to work as true collaborators in enhancing outcomes while limiting costs.
” Agentic AI also has the potential to address one of the most persistent political divides in U. S. healthcare: the tension between individual and collective solutions,” Connely said”. Healthcare dollars have traditionally been allocated to broad population cohorts because of the inability to make decisions at individual levels, which is inefficient and prone to fraud and waste.
” AI changes this dynamic by enabling real-time hyper-personalization”, he added. It enables payers to take into account individual circumstances and apply customary rules and interventions that are tailored to the individual’s needs at the time. This method combines the effectiveness of targeted, data-driven care with the fairness of policies intended to help everyone.
By enabling precise, personalized decision making, AI aligns individual care with broader social justice goals, ensuring resources are used more effectively, he said.
” AI technology is evolving rapidly, offering payers new ways to engage with providers, members and social systems”, Connely concluded. ” These advancements are laying the groundwork for value-based care to become the standard in U. S. healthcare, delivering on its promise to improve outcomes, reduce costs and transform the system for the better”.
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